energies
Article
Land Suitability Analysis for Solar Farms
Exploitation Using GIS and Fuzzy Analytic Hierarchy
Process (FAHP)—A Case Study of Iran
Ehsan Noorollahi, Dawud Fadai *, Mohsen Akbarpour Shirazi and Seyed Hassan Ghodsipour
Industrial Engineering Department, Amirkabir University of Technology, 424 Hafez Ave, Tehran 15916-34311,
Iran; e.noorollahi@aut.ac.ir (E.N.); m.a.shirazi@gmail.com (M.A.S.); ghodsypo@aut.ac.ir (S.H.G.)
* Correspondence: fadaid@aut.ac.ir; Tel.: +98-09121275953
Academic Editors: Senthilarasu Sundaram and Tapas Mallick
Received: 5 June 2016; Accepted: 8 August 2016; Published: 19 August 2016
Abstract: Considering the geographical location and climatic conditions of Iran, solar energy can
provide a considerable portion of the energy demand for the country. This study develops a two-step
framework. In the first step, the map of unsuitable regions is extracted based on the defined
constraints. In the next step, in order to identify the suitability of different regions, 11 defined
criteria, including solar radiation, average annual temperatures, distance from power transmission
lines, distance from major roads, distance from residential area, elevation, slope, land use, average
annual cloudy days, average annual humidity and average annual dusty days, are identified. The
relative weights of defined criteria and sub-criteria are also determined applying fuzzy analytical
hierarchy process (FAHP) technique. Next, by overlaying these criteria layers, the final map of
prioritization of different regions of Iran for exploiting solar photovoltaic (PV) plants is developed.
Based on Iran’s political divisions, investigation and analysis of the results have been presented for a
total of 1057 districts of the country, where each district stands in one of the five defined classes of
excellent, good, fair, low, and poor level. The obtained data indicate that 14.7% (237,920 km2 ), 17.2%
(278,270 km2 ), 19.2% (311,767 km2 ), 11.3% (183,057 km2 ), 1.8% (30,549 km2 ) and 35.8% (580,264 km2 )
of Iran’s area are positioned as excellent, good, fair, low, poor and unsuitable areas, respectively.
Moreover, Kerman, Yazd, Fars, Sisitan and Baluchestan, Southern Khorasan and Isfahan are included
in the regions as the most excellent suitable provinces for exploiting solar PV plants.
Keywords: land suitability; solar energy; PV; GIS; fuzzy AHP; spatial analysis
1. Introduction
Nowadays, challenges such as limitation of fossil fuels, environmental pollution, importance of
energy-mix diversification, and possibility of earning more value from fossil resources have encouraged
governments all over the world to promote the renewable energy share in their energy portfolios.
The representatives of 195 countries in the 21st conference of the countries committed to climate
change convention in Paris climate conference (COP21) in December 2015 reached a legal and binding
agreement for determining the environmental situation of the earth. In this conference, development
in the consumption of fossil fuels such as oil and gas was considered to be the main reason for the
emission of greenhouse gases and global warming. Based on the final COP21 statement, world’s
countries have committed themselves to maintain increase in the Earth’s temperature to be lower than
2 ◦ C. Moreover, the countries should try to keep it under 1.5 ◦ C [1].
Iran, with an annual production of 850 million tons of greenhouse gases, is one of the 10 major
countries in the world in terms of GHG production. According to the commitments necessitated in
COP21, Iran will try to reduce about 190 million tons of the volume of its greenhouse gases in the next
Energies 2016, 9, 643; doi:10.3390/en9080643
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15 years, i.e., by 2030. Achieving this legally binding commitment by exploiting renewable energies as
a suitable solution has been the agenda of energy planners and policymakers [2].
Iran, as one of the wealthiest countries in the world, not only holds considerable amount of fossil
sources, but also has great potential of renewable energy sources. Nonetheless, based on the current
circumstances of energy mix and the intensity of energy consumption, sole reliance on the oil and
natural gas is a strategic fault that may result in irreparable issues in the future.
Solar energy is one of the best and most economical types of renewable energies in Iran. It will not
only lead to a reduction in most of the human concerns such as environmental pollution, new emerging
diseases and termination of fossil resources, but also considering Iran’s climatic conditions, it can be
well developed in Iran. Generally, Concentrating Solar thermal Power (CSP) and photovoltaics (PV)
are the two major technologies for exploiting solar energy. According to studies in an area of 2000 km2 ,
there is the possibility of installing 60 GW of solar thermal power plant in Iran. In recent years,
the utilization of solar energy systems is developing because of its simplicity, easy transport, high
reliability, absence of mechanical parts, compatibility with the environment as well as no need for
fuel [3].
Identifying suitable locations for the construction of solar farms, as with any other engineering
project, requires detailed information and accurate planning. In the process of exploiting solar energy,
alongside the necessity of existence of solar insolation potential in the region, the vital point is
investigating different places considering various technical, socioeconomic, and environmental criteria.
Geographic Information System (GIS) is considered as a very useful and practical tool capable of
developing a database which can act as the departure point for guiding any decision support system
(DSS) [4].
In this study, the criteria defined using literature and expert opinions have been classified into
four categories: climatology, environment, location, and meteorology. Considering the necessity of
taking economic, technical, social, and environmental constraints into account in solar site selection,
this study develops a two-step framework.
In the first step, the map of unsuitable regions is prepared based on the related constraints
such as urban lands, cultural heritage, watercourses, wildlife, conservation areas, national parks,
etc. In the next step, in order to identify the suitability of different regions remaining, 11 criteria,
including solar insolation, average annual temperatures, distance from power transmission lines,
distance from major roads, distance from residential area, elevation (altitude above sea level), slope,
land use, average annual cloudy days, average annual humidity and average annual dusty days are
identified. The relative weights of these criteria and sub-criteria are also determined utilizing Fuzzy
Analytical Hierarchy Process (FAHP) technique. Next, by overlaying these criteria layers, the final map
of suitability of different regions of Iran for exploiting solar PV plants is developed. Investigation and
analysis of the results will be presented for 1057 districts of the country according to Iran’s political
divisions. Finally, each district stands in one of the five defined classes of excellent, good, fair, low, and
poor level.
This paper has been organized as follows: Section 2 reviews the literature of solar site selection.
Section 3 overviews the Iran’s current status and outlook of energy sector. Section 4 describes the
proposed framework. We present the results and discussion in Section 5. Finally, the conclusion is
given in Section 6.
2. Literature Review
Because of various issues and characteristics of utilized energy resources in electricity generation
industry, a multi-lateral survey is needed for comprehensive evaluation. Hence, multi-criteria decision
analysis (MCDA) techniques have become increasingly prominent in sustainable energy planning
to handle the conflicts of decision makers’ opinions and the complication of criteria [5]. A review of
the literature shows that several studies in different fields such as energy management, renewable
energies planning, energy resource allocation, etc. have applied various multi attribute decision
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making (MADM) techniques [6]. A model to rank various renewable and non-renewable electricity
production technologies was proposed by Stein by utilizing the analytic hierarchy process (AHP) based
on four comprehensive criteria: financial, technical, environmental and socio-economic-political [7].
In order to evaluate the energy alternatives and create a roadmap of energy strategies for Turkey,
Erdogan et al. [8] proposed a hybrid MCDM methodology that included AHP and TOPSIS based on
type-2 fuzzy sets.
With regard to surveying potential sites for exploiting renewable energies in Iran, several studies
have been carried out. Alamdari et al. [9] examined the feasibility of exploiting solar energy for
different regions of Iran based on information obtained from 63 weather stations and application of
radiation maps in GIS. Based on the results, the greatest radiation is related to central and southern
regions of Iran except for the southern coastal regions. Existence of a radiation of 500 W/m2 indicates
the good potential of these regions for exploiting photovoltaic systems.
The current status and prospect of exploiting solar energy in Iran have been dealt with in [3].
Considering the area of 1.6 × 1012 m2 and having 300 sunny days in Iran along with the mean radiation
of 2200 kWh/m2 , there are suitable conditions for the expansion of solar energy industry in Iran.
Currently, solar energy units in Iran are established in Shiraz, Semnan, Taleghan, Yazd, Tehran,
and Khorasan.
A GIS-based integrated framework for determining the best size and location related to PV plants
is presented utilizing mathematical optimization and simulation besides considering the criteria such
as solar radiation, slope, elevation, and aspect. Kucuksari et al. determined the candidate places
and thereafter, via mathematical modeling, presented long-term expansion plan of this technology.
Moreover, based on simulation, the voltage profile related to a distribution network is investigated [10].
In order to identify the best location for the exploitation of wind farms, Sánchez-Lozano et al. [11]
proposed an integrated model based on GIS and fuzzy Multi-Criteria Decision Making (MCDM)
technique. They utilized Fuzzy Analytic Hierarchy Process (FAHP) to determine the relative
importance of the criteria, and GIS was also applied to generate the database of the alternatives.
Because of site selection of large ground-mounted PV plants, a GIS based model to identify the
suitable areas for the installation of photovoltaic systems in Piedmont region, Italy was proposed
by Borgogno Mondino et al. [12]. Several criteria including qualitative and quantitative criteria
were considered and Artificial Neural Network (ANN) was applied for the aggregation of the
quantitative criteria.
Prioritization of different regions for exploiting solar energy in Konya Province in Turkey has been
evaluated based on environmental and economic factors by [13]. In this study, for the environmental
criteria, three sub-criteria, distance from residential areas, land use, and distance from roads, have
been defined. Similarly, considering economic factors, the sub-criteria of slope and distance from
transmission lines have been considered. Using AHP method, the priority of each criterion is defined
and based on the obtained relative importance weights, digitized layers are overlaid and the suitable
regions for the utilization of solar energy have been determined.
In order to locate solar farms in the south of Spain, Sánnchez-Lozano et al. [14] determines
prioritization of the candidate places via ELECTRE-TRI technique based on defined criteria and
utilizing the GIS software. The evaluations have been carried out based on four criteria including
environmental, location, climatology and orography.
In order to determine the best location for placement of solar thermoelectric installments, GIS and
AHP were utilized for determining the weights of criteria. Thereafter, evaluation and prioritization of
10 candidate regions was carried out by fuzzy TOPSIS [15].
By utilizing spatial multi-criteria evaluation and fuzzy sets theory, Charabi and Gastli [16]
evaluate the suitability of large farmlands for different PV technologies in Oman by taking nine
technical, economical, and environmental criteria into consideration for evaluating the regions of
interest. The results indicated that 0.5% of the total lands in Oman have a high suitability level for
installing photovoltaic plants.
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A new assessment framework based on GIS was developed in [17] to characterize PV deployment
in the UK. They investigated the temporal and spatial expansion trends for various PV market sections,
including domestic and non-domestic rooftop and grid connected solar farms. The obtained results
revealed the strong correlation between expansion of PV technology and level of policy support.
To investigate the performance of solar farms, a hybrid framework model based on DEMATEL,
DANP and GIS is proposed by Chen et al. [18]. They considered four dimensions, including
environment, orography, location and climatology, as well as ten criteria. Based on their results,
the orography dimension (slope, orientation and area) in solar energy industry should improve first
for enhancing the performance of solar farms.
The potential of solar insolation in Iran and the extraction of the solar radiation maps for various
modes so as to implement PV and CSP technology was examined by [19]. Then, feasibility study for
the construction of a 5 MW photovoltaic plant in 50 cities of Iran was carried out, where, according
to the obtained results, central and southern regions of Iran have the greatest priority. Because of
solar site selection in Isfahan, Iran and based on GIS and multiple criteria decision making, the most
suitable areas for the exploitation of solar energy, such as Borkhar, Nain, Shahin Shahr and Meimeh,
were identified [20].
In order to assess the potential of European regions for solar power generation, Castillo et al.
using multi-criteria decision making and GIS, took into consideration criteria such as solar radiation,
elevation, aspect, slope, land use, population distribution, and proximity to the power transmission
line to prioritize various regions and extract the suitability map for PV power plants. The results
obtained revealed that there is no correlation among the EU investment and the suitability in solar
energy [21].
In order to investigate the various energy generation scenarios in the province of Frosinone,
Massimo et al. developed and experimented a model based on Geographical Information Database
System (GIS DB) due to new distributed generation technologies. Their detailed examination is done
for solar energy based on the availability of land use to estimate the appropriate potential, the amount
of residential electricity consumptions and the integration of renewable energy sources [22].
Potential solar assessment for concentrating solar power (CSP) and on-grid PV technology was
studied by [23]; using satellite-derived data and GIS based approach, reanalysis data and measurements
were combined for solar mapping and finally the potential maps of Vietnam were generated.
Appropriate use of solar energy involves the identification of regions with high potential for
utilizing this energy. Although most regions in Iran have good potential for solar energy, identifying
the areas with suitable condition can increase the efficiency of electricity generation.
3. Iran’s Status and Outlook of Energy Sector
Iran, with an area of 1,640,195 km2 and 79 million inhabitants, is situated in latitude 35◦ 440 N and
longitude 51◦ 300 E. Based on the political division, Iran has 31 provinces, 429 counties, 1057 districts,
and 2589 rural districts [24]. Iran is the 18th largest country in the world where over half of the
country’s area consists of mountains and altitudes, one fourth is plains, and less than one fourth of it
consists of lands under cultivation. In terms of climate, Iran is one of the most unique countries. The
temperature difference in winter between the warmest and coldest points sometimes reaches more
than 50 ◦ C. The hottest points of the earth in 2004 and 2005 were measured in Loot desert in Iran [9].
Due to increase in population growth, social welfare and low energy prices, power consumption
in Iran is inefficient and is rapidly increasing. The ultimate demand of 301 million tons of oil equivalent
energy (twice the current demand) in 2035 is predicted for Iran [25].
In 2013–2014, the total electric energy generated by Iran’s power plants amounted to 263 GWh
with 3.1% growth compared with the previous year. This amount was supplied by power plants in
the Ministry of Energy, large industries and the non-governmental sector as follows: 25 steam power
plants, 78 gas turbine power plants including 18 DG units (CHP-DG), 18 combined cycle power plants,
46 diesel power plants, 48 hydro power plants (large, medium, small and mini), 182 wind turbines,
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four photovoltaic units and three biogas power plants. Note that the share of thermal plants was
92.6%, hydro plants reached 5.5% and renewable and nuclear plants was about 1.9% [26]. Based on
the Five-Year Socio-Economic and Cultural Development Plan, some focal points of electric power
industry are promotion of the renewable energy share, reduction of loss, improvement of power plant
efficiency and load management, all of which have been put on the agenda of power industry [25].
Considering the position of Iran in the world’s dry and desert regions, existence of climate
diversity in Iran and solar radiation in majority of the regions and in the majority of seasons have
provided a suitable and necessary context for utilizing and developing solar energy; such that using
1% of the entire area of the country, it is possible to generate nine million megawatt hours of energy
per day. The overall potential of solar energy and energy generation in Iran is 14.7 TWe [3].
The potential of solar radiation in Iran is much more than that in Germany, one of the leading
countries in solar energy [27]. Therefore, with such potential, the negligible share of solar energy
in the Iran’s energy mix looks irrational; thus, giving attention to the development of this industry
is necessary.
4. The Proposed Framework
In this study, in order to identify the suitable regions for exploiting solar energy in Iran, GIS
with multi-criteria decision-making approach was utilized. In this study, based on Iran’s political
divisions, the conducted investigations were performed where the territory is divided into 1057 districts
(the district is similar to a district in England) and the land suitability index (LSI) of each district is
determined. In summary, the proposed methodology includes the following stages:
√
Establishment of a team consisting of academic, governmental, and industrial experts who will
take part in the process of identifying and evaluating the criteria
√
Identification of the technical, economic, social, and environmental constraints for exploiting
solar energy in different regions of the country for identifying the unsuitable areas
√
Generating the layers associated with defined constraints by GIS
√
Overlaying of layers in GIS with AND logic and preparation of the map of unsuitable regions in
the country
√
Identifying and evaluating the criteria influencing the solar energy potential for land suitability
analysis modeling
√
Determining the weight of evaluation criteria using FAHP
√
Preparation of the layers related to criteria layer in GIS
√
Overlaying of layers in GIS via Simple Additive Weight (SAW) method and preparation of the
land suitability map of regions for exploiting solar energy
All digitization, conversion, and analysis of maps processes were performed by Arc GIS Desktop
software. The calculations related to FAHP were carried out utilizing Super Decision software following
the determination of crisp value for experts’ opinions.
4.1. Identifying the Unsuitable Area
At this stage, the aim is to investigate and perform feasibility study of different regions of the
country for the implementation of solar PV plants. In order to achieve this objective, GIS is utilized.
GIS is a very useful instrument that analyzes and visualizes geospatial information and can be used
for guiding decision-support systems for identification of suitable spatial locations by developing
a database.
In the planning process of developing solar PV plants, taking some technical, economic, social,
and environmental constraints into consideration for removing inappropriate regions is necessary [28].
For instance, land is one of the main infrastructures needed for the construction of plants and this
issue has gain more significance for solar plants because, on average, total land-use requirements for
PV is 7.9 acres/MW [29].
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In general, in order to establish a solar farm, adhering to suitable buffer considering constraints
such as the following bounds is necessary. Violation of each of these conditions makes the application
of the under study region practically unacceptable or uneconomical [29]:
√
√
√
√
√
√
√
√
√
Regions with a solar radiation lower than 1300 (kWh·m−2 ·year−1 )
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2016, 9,
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6 of 23
Regions
with
wildlife conservation areas, and national parks
 Regions with a lower distance of 2 km from protected regions such as national natural
Regions located closer to the minimum distance determined for the criteria of cities and populated
monuments, wildlife conservation areas, and national parks
centers (the minimum distance should be 2000 m from cities and 500 m from villages)
 Regions located closer to the minimum distance determined for the criteria of cities and
Land-use
such as forest, agricultural lands, cannot be suitable options for the construction of
populated centers (the minimum distance should be 2000 m from cities and 500 m from villages)
plant such as forest, agricultural lands, cannot be suitable options for the construction of
 solar
Land-use
Regions
with a distance more than 50 km from roads and power transmission lines
solar plant
than
0.550km
km transmission
from roads lines
 Regions
Regionswith
witha adistance
distanceless
more
than
kmfrom
fromfaults
roads and
and 0.1
power
 Regions
Regionswith
witha adistance
distanceless
lessthan
than 10.5
km
fromrivers,
faultslakes,
and 0.1
km from and
roads
km
from
wetlands,
dams
 Regions
Regionswith
withan
a distance
less
than
1
km
from
rivers,
lakes,
wetlands,
and
dams
elevation (altitude above sea level) of over 2200 m
 Regions
Regionswith
witha an
elevation
(altitude
above
sea level) ofasover
2200 m area
slope
greater
than 11%
is considered
unsuitable
 Regions with a slope greater than 11% is considered as unsuitable area
These
restrictions
these layers
layersare
arethen
thenoverlaid
overlaidbased
based
AND
logic.
These
restrictionsare
areanalyzed
analyzedin
in GIS,
GIS, and
and these
onon
AND
logic.
According
to
the
results
obtained
from
overlaying
of
these
layers,
the
suitable
regions
for
developing
According to the results obtained from overlaying of these layers, the suitable regions for developing
solar
energy
are
identified.
restricted layers
layersare
areillustrated
illustratedininFigure
Figure
1 and
solar
energy
are
identified.Some
Someof
of the
the defined
defined restricted
1 and
thethe
final
result
of of
the
combination
presentedin
inFigure
Figure2.2.
final
result
the
combinationofofrestricted
restrictedcriteria
criteria layers
layers is presented
(a)
(b)
(c)
(d)
Figure 1. Cont.
Figure 1. Cont.
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(e)
(e)
(f)
(f)
Figure 1. Restrictive criteria layers: (a) prohibited area; (b) protected zones; (c) residential area;
Figure
(a) prohibited
prohibited area; (b) protected zones; (c) residential area;
Figure 1.1. Restrictive criteria layers: (a)
(d) frosty zones; (e) sloped area; and (f) faults buffer.
(d)
frosty
zones;
(e)
sloped
area;
and
(f)
faults
(d) frosty zones; (e) sloped area; and (f) faultsbuffer.
buffer.
Figure 2. Final map of unsuitable areas.
4.2. Evaluation Criteria
Figure 2. Final map of unsuitable areas.
Figure 2. Final map of unsuitable areas.
In general,
site selection criteria are normally studied in the form of different groups including
4.2.
Criteria
4.2. Evaluation
Evaluation
Criteria
environmental, economic, geographical, demographic, land-use, hydrological, security, technical,
In
site
criteria
are
normally
in
form
of
groups
including
InIngeneral,
general,
site selection
selection
criteria
arestudies
normally
studied
in the
thethe
form
of different
different
groups
including
etc.
this research,
according
to other
andstudied
by reviewing
criteria
based on
positioning
of
environmental,
economic,
geographical,
demographic,
land-use,
hydrological,
security,
technical,
environmental,
economic,
hydrological,
technical,
solar farms through
Delphigeographical,
method, somedemographic,
questionnaires land-use,
were prepared
and given security,
to four experts
toetc.
In
this
research,
according
to
other
studies
and
by
reviewing
the
criteria
based
on
positioning
etc.respond.
In this research,
according
towere
otherobtained
studies through
and by reviewing
the criteria
based
on positioning
of
As many as
14 criteria
expert judgments
by this
method.
In order to of
solar
farms
through
Delphi
method,
some
questionnaires
were
prepared
and
given
to
four
experts
to
prevent
further
complication
of
the
study,
three
criteria
with
the
lowest
score
were
removed
and
the
solar farms through Delphi method, some questionnaires were prepared and given to four experts to
respond.
As
as
criteria
were
through
expert
judgments
by
this
method.
In
remaining
11 criteria
classified
based
on the
hierarchy
structure
shown
Figure
3. Next,
some to
respond.
As many
many
as 14
14were
criteria
were obtained
obtained
through
expert
judgments
byin
this
method.
In order
order
to
prevent
further
complication
of
three
with
explanations
presented regarding
the defined
criteria.
prevent
furtherare
complication
of the
the study,
study,
threecriteria
criteria
with the
the lowest
lowest score
score were
were removed
removed and
and the
the
remaining
remaining 11
11 criteria
criteria were
were classified
classified based
based on
on the
the hierarchy
hierarchy structure
structure shown
shown in
in Figure
Figure3.
3. Next,
Next, some
some
explanations
are
presented
regarding
the
defined
criteria.
explanations are presented regarding the defined criteria.
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Figure 3. Evaluation criterion for location of solar PV plants.
Figure 3. Evaluation criterion for location of solar PV plants.
4.2.1. Solar Radiation (C1)
Figure 3. Evaluation criterion for location of solar PV plants.
4.2.1. Solar Radiation (C1)
To evaluate the optimal location to implant PV systems, solar radiation is one of the most
4.2.1.
Solar Radiation
(C1) location to implant PV systems, solar radiation is one of the most
To
evaluate
the optimal
important
factors
that determine
whether the candidate locations will receive sufficient sunlight
throughout
the that
year.
Generally,
PV
systems
efficiency
higher
in solar
sunnier
regions,
asufficient
rule
thumb,
important
whether
the
candidate
locations
will
receive
sunlight
Tofactors
evaluate
thedetermine
optimal location
to implant
PVis systems,
radiation
isasone
of of
the
most
PV
systems
require
a minimum
solar
radiation
of 1300 kWh
· m−2 · year
(3.5 kWh
· m−2 · day
important
determine
whether
theefficiency
candidate
will
receive
sufficient
sunlight
throughout
thefactors
year. that
Generally,
PV
systems
islocations
higher
in −1
sunnier
regions,
as−1a) for
rule of
−
2
−
1
−
2
economical
operation
[30],
hence,
in
this
study
we
remove
areas
with
solar
radiation
less
than
1300
throughout
the
year.
Generally,
PV
systems
efficiency
is
higher
in
sunnier
regions,
as
a
rule
of
thumb,
thumb, PV systems require a minimum solar radiation of 1300 kWh·m ·year (3.5 kWh·m ·year−1 )
−1
−1
kWh
· m−2 · year
as illustrated
in solar
Figure
4. In study
addition,
little
inwith
PV(3.5
system
arises
PV systems
require
a minimum
of 1300
kWh decrease
· m−2areas
· year
kWh efficiency
·radiation
m−2 · day −1
) for than
for economical
operation
[30], hence,
inradiation
this
wea remove
solar
less
−2
−1
when
solar
irradiation
exceed
2000
kWh
·
m
·
year
,
due
to
the
negative
effect
of
higher
ambient
economical
operation
[30],
hence,
in
this
study
we
remove
areas
with
solar
radiation
less
than
1300
−1 as illustrated in Figure 4. In addition, a little decrease in PV system efficiency
1300 kWh·m−2−2 ·year
−1 PV module efficiency [31].
temperatures
on
kWh · m · year
as illustrated in Figure 4. In addition, a−1little decrease in PV system efficiency arises
arises when solar irradiation exceed 2000 kWh·−2m−2 ·year
, due to the negative effect of higher ambient
when solar irradiation exceed 2000 kWh · m · year −1 , due to the negative effect of higher ambient
temperatures
on
PV
module
efficiency
[31].
temperatures on PV module efficiency [31].
Figure 4. The conceptual model of the proposed framework.
Figure
4. The
conceptualmodel
model of
Figure
4. The
conceptual
of the
theproposed
proposedframework.
framework.
Energies 2016, 9, 643
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4.2.2. Average Annual Temperatures (C2)
One of the components of photovoltaic systems is the solar panel. In order for the system
to provide the required power, it should be properly designed. One of the issues that influence
determination of the size of the required panel is its efficiency. The efficiency of solar panels depends
on its temperature and the panel’s temperature itself results from the ambient temperature and the
intensity of the solar radiation. The performance of the modules of PV systems declines with increase
in ambient temperature. For every 1 ◦ C rise in the cell temperature at temperatures above 25 ◦ C,
the amount of generated energy declines by about 0.4%–0.5% [32,33].
4.2.3. Distance from Power Transmission Lines (C3)
Considering the high costs associated with construction of power transmission lines, one of the
important criteria in siting of solar farms is distance from transmission lines. Overall, electric power
transmission lines influence the positioning of solar farms in terms of safety, network security, and
quick accessibility for installing equipment and potential repairs. Studies have demonstrated that the
best distance from power networks for the safety of solar farms and the network is 0–3 miles. In other
words, the proximity of the location of plant construction to electric energy transmission lines is an
important economical advantage [30]. In this study, regarding the extent of understudy area and also
the potential of central and desert regions in Iran for utilization of solar energy in order to reduce the
possibility of eliminating potential areas, the last defined level in this criterion belongs to areas with
distance of 20 to 50 km from power transmission lines with a total weight of (0.012). In other words,
about 90% of relative weight belonging to this criterion is allocated to regions less than 12.5 miles from
transmission lines [34]. In summary, regions at a distance more than 50 km from power transmission
lines has been considered as unsuitable areas for solar farms establishment, as illustrated in Figure 4.
4.2.4. Distance from Major Roads (C4)
Construction of new access roads for transportation of goods and equipment is very expensive
and is one of the unavoidable factors in the construction of solar plants. Certainly, the easier is the
access to the plant, the lower the cost of plant construction will be. Regarding this criterion, according
to the wide range of the understudy area (1,640,195 km2 ) and also the availability of local roads across
the country, the last level defined in this criterion belongs to areas with distance of 30 to 50 km from
main access roads with total weight of (0.009). In other words, about 90% of relative weight of this
criterion is allocated to areas with distance less than 18.75 miles [34]. Thus, in order to reduce the
possibility of eliminating suitable areas from investigation, in this study, the maximum and minimum
distance from major roads in positioning of solar farms has been considered as 50 km and 0.1 km,
respectively [15].
4.2.5. Distance from Residential Areas (C5)
Due to the various unfavorable environmental impacts on the populated centers and urban
growth, in this research, distance from residential areas is considered as one of the important criteria
in solar PV farms site selection. Therefore, solar farms are at a distance less than 2000 m from city and
500 m from village areas must not be established. Moreover, regions at a distance more than 45 km
from populated centers are considered as unsuitable areas for solar farms establishment, as illustrated
in Figure 4.
4.2.6. Elevation (C6)
The thickness and the compounds of atmosphere influence the entrance of both shortwave energy
of the sun and longwave energy of the earth. The lower the elevation of a region from sea level,
the greater the atmosphere thickness. Thus, elevated regions enjoy a greater solar radiation potential
than lower regions due to the fact that they receive a great deal of energy [35].
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4.2.7. Slope (C7)
Slope is one of the highly important factors in site selection of PV farms, whose mean value in
Iran is about 11%. In general, lands with a slope greater than 4% have a lower priority since panels
shadow the next row and adversely affect the system efficiency [28].
4.2.8. Land Use (C8)
One concern regarding establishment of solar farms is land use as an important environmental
factor for site selection. In this paper, land use was evaluated for five level as barren, rangeland, shrub,
rainfed and irrigated areas. Barren areas were considered as the best areas and irrigated areas have the
lowest priority for exploitation solar farms.
4.2.9. Average Annual Cloudy Days (C9)
One of the criteria used in this research is the annual average of cloudy days in the year. As the
number of cloudy days has a great effect on the number of sunny hours and the performance of panels,
the higher the average annual of cloudy days in a region, the lower the value of generation due to
reduction of the received radiation; this value can even reach zero [36].
4.2.10. Average Annual Relative Humidity (C10)
Humidity is the amount of water present in the air. Humidity affects the irradiance level of
sunlight and ingression to the solar cell enclosure. Thus, the increase in humidity is inversely related
with the efficiency of solar panels [37].
4.2.11. Average Annual Dusty Days (C11)
Dust is defined as the minute solid particles less than 500 µm in diameter. Dust deposition is a
function of several environmental and weather conditions that affect the efficiency of PV. This particle
absorbs 15% of shortwave energy of the sun. Considering the environmental changes of the recent years
and entrance of dust systems in the southern and southwestern regions of Iran, for exploiting solar
energy, the regions that have the least annual average of dusty days in the year are more suitable [37].
4.3. Fuzzy Analytic Hierarchy Process (FAHP)
The analytic hierarchy process (AHP) was introduced by Saaty [38] and broadly utilized by
several researches in various fields to deal with the complexity of many problems. Several researchers
utilized the AHP method and presented a framework for prioritizing the alternatives [13].
AHP method does not take into account the uncertainty associated with the process. To alleviate
this imperfection, several researchers have turned to use the fuzzy sets theory. The fuzzy set
theory, presented by Zadeh [39], takes into consideration the vagueness, imprecision and uncertainty
associated with the process.
In this research, the relative weights of defined criteria and sub-criteria will be determined
utilizing FAHP technique. Thereafter, the final map of land suitability of different regions of Iran is
obtained by overlaying these criteria layers based on the calculated weights.
The expert team consists of four governmental, industrial and academic experts. Experts’
judgments were gathered utilizing the prepared questionnaires. In order to use fuzzy theory, first,
experts are asked to express pair wise comparisons with linguistic terms as indicated in Table 1.
Thereafter, the linguistic variables were transformed into triangular fuzzy numbers.
Energies 2016, 9, 643
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Table 1. Linguistic variables for the weightings.
Linguistic Term
TFN
Very high (VH)
High (H)
Medium high (MH)
Medium (M)
Medium low (ML)
Low (L)
Very low (VL)
(0.9, 1.1)
(0.7, 0.9, 1)
(0.5, 0.7, 0.9)
(0.3, 0.5, 0.7)
(0.1, 0.3, 0.5)
(0, 0.1, 0.3)
(0, 0, 0.1)
Based on the experts’ judgments, crisp relative importance weights of the criteria can be calculated
by obtained fuzzy importance weights based on the membership functions in Table 1. Aggregated
triangular fuzzy numbers (TNF) are calculated by unifying the experts’ opinions with geometric mean.
An aggregate triangular fuzzy number Fe is obtained by unifying the experts’ opinions by utilizing the
geometric mean method as illustrated in Equation (1):

1
K
K
1
K
K
K
1
K

e =  ( ∏ lt ) , ( ∏ mt ) , ( ∏ ut ) 
F
t =1
t =1
(1)
t =1
where, (lt , mt , ut ) is the expert t viewpoint (t = 1, 2, 3, 4). We apply the center of gravity (COG) method
to defuzzify the aggregate triangular fuzzy numbers as in Equation (2):
n∗ =
l+m+u
[(u − l ) + (m − l )]
+l =
3
3
(2)
After determining the aggregated pairwise comparison matrix, priorities could be calculated as:
Ws .ws = λmax .ws
(3)
where, Ws is the aggregated comparison matrix, ws is the eigenvector, and λmax is the largest eigenvalue
of matrix Ws .
The consistency of results is crucial for more accurately represented judgments. Consistency of
result at each step and for each expert’s opinion was calculated by consistency index (CI) and if there
was any inconsistency, the expert was asked to amend the conflicting part from the survey. CI index
and consistency ratio (CR) can be calculated as illustrated in Equations (4) and (5) where, n is the
size of pairwise comparison matrix and RI is random index. When CR ≤ 0.1, the consistency test is
passed [13].
λmax − n
(4)
CI =
n−1
CR =
CI
R
(5)
The results of the calculated weights for defined criteria and sub criteria using FAHP technique
are summarized in Table 2.
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Table 2. The calculated weights for defined criteria and sub criteria by FAHP.
Goal
Obj.
Weight
Criteria
C1: Solar radiation (kWh·m−2 ·year−1 )
Climatology
Sub–Criteria
Weight
Final Weight
0.275
1300–1700
1900–1700
2000–1900
2100–2000
>2100
0.09
0.19
0.26
0.24
0.22
0.025
0.052
0.072
0.066
0/061
0.071
24–25
25–26
26–27
27–28
>28
0.3
0.26
0.19
0.15
0.1
0.021
0.018
0.013
0.011
0.007
0.112
20–50
15–20
10–15
5–10
0–5
0.11
0.13
0.16
0.26
0.34
0.012
0.015
0.018
0.029
0.038
0.0882
30–50
20–30
10–20
5–10
0–5
0.1
0.14
0.16
0.26
0.34
0.009
0.012
0.014
0.023
0.030
0.081
30–45
20–30
15–20
10–15
3–10
0.11
0.145
0.16
0.235
0.35
0.009
0.012
0.013
0.019
0.028
0.346
C2: Average Annual Temperatures (◦ C)
Land suitability for
location of solar PV plants
C3: Distance from power transmission lines (km)
Location
Weight
0.2812
C4: Distance from major road (km)
C5: Distance from residential area (km)
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Table 2. Cont.
Goal
Obj.
Weight
Criteria
C6: Elevation (m)
Environmental
0.231
C7: Slope (%)
C8: Land use
C9: Average annual cloudy days
Meteorology
0.1472
C10: Average annual relative humidity (%)
C11: Average annual of dusty days
Weight
Sub–Criteria
Weight
Final Weight
0.081
200–0
450–200
750–450
1200–750
1200–2200
0.055
0.08
0.125
0.22
0.52
0.004
0.006
0.010
0.018
0.042
0.08
<1
1–2
2–3
3–4
4–11
0.445
0.25
0.155
0.098
0.052
0.036
0.020
0.012
0.008
0.004
0.07
Barren
Rangeland
Shrub
Rainfed Land
Irrigated land
0.75
0.1
0.08
0.05
0.02
0.053
0.007
0.006
0.004
0.001
0.058
170–120
120–80
80–50
50–30
30–12
0.04
0.07
0.15
0.29
0.45
0.002
0.004
0.009
0.017
0.026
0.041
83–60
60–50
42–50
42–35
35–26
0.09
0.13
0.18
0.23
0.37
0.004
0.005
0.007
0.009
0.015
0.0482
>120
120–70
70–50
50–30
<30
0.05
0.1
0.15
0.2
0.5
0.002
0.005
0.007
0.010
0.024
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4.4. Land Suitability Analysis Modeling
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In this section, various information; the criteria investigated utilizing GIS as decision-support
system
and, according
to the criteria, restrictions, and problems of constructing solar
4.4. (DSS);
Land Suitability
Analysis Modeling
farms, the calculated relative weights were combined and integrated with each other, followed by
In this section, various information; the criteria investigated utilizing GIS as decision-support
determination
of the prioritization of 1057 districts around the Iran. A conceptual model of the
system (DSS); and, according to the criteria, restrictions, and problems of constructing solar farms,
proposed
framework
is shown
in Figure
the calculated relative
weights
were 4.combined and integrated with each other, followed by
The
information
obtained
from 72
stations
around
the
country
has model
been utilized
determination
of the
prioritization
of synoptic
1057 districts
around
the Iran
. A
conceptual
of the for
the preparation
of criteriais layers
and
proposed framework
shown in
in GIS
Figure
4. zoning map is also prepared for various defined criteria.
information
from prepared
72 synopticand
stations
country
has beenmaps
utilized
for the
The mapsThe
related
to eachobtained
factor were
usedaround
in the the
form
of digitized
at the
scale of
preparation
of
criteria
layers
in
GIS
and
zoning
map
is
also
prepared
for
various
defined
criteria.
The
1:50,000. In addition, the map of elevation and slope has been generated based on digital elevation
maps
related
to each factor were prepared and used in the form of digitized maps at the scale of
model
of the
country.
1:50,000. In addition, the map of elevation and slope has been generated based on digital elevation
As in the majority of synoptic stations around the country, the amount of solar radiation was not
model of the country.
measured, in this research for this criterion, the data provided by National Oceanic and Atmospheric
As in the majority of synoptic stations around the country, the amount of solar radiation was
Administration
(NOAA) of the US were utilized.
not measured, in this research for this criterion, the data provided by National Oceanic and
According
the criteria and
sub-criteria
in Table 2, the relevant criteria layers are
Atmosphericto
Administration
(NOAA)
of the US defined
were utilized.
prepared According
as illustrated
in
Figure
5.
In
order
to
generate
the final
suitability
map, by
overlaying
to the criteria and sub-criteria defined in Table
2, the
relevant criteria
layers
are
theseprepared
layers and
selecting
common
sections
in
all
the
layers
based
on
the
defined
classes
well as
as illustrated in Figure 5. In order to generate the final suitability map, by overlaying as
these
layers and
selecting common
sections
in allinthe
layers
on map
the defined
classes
as well
as
considering
the restricted
regions as
illustrated
Figure
2, based
the final
related to
Iran land
suitability
consideringasthe
restrictedinregions
was generated
illustrated
Figure as
6. illustrated in Figure 2, the final map related to Iran land
suitability
was generated
as illustrated
in Figure
In
this research,
the values
determined
for 6.
Land Suitability Index (LSI) for each district were
In this research, the values determined for Land Suitability Index (LSI) for each district were
determined based on the share of each of the five defined classes in each district area, where higher
determined based on the share of each of the five defined classes in each district area, where higher
scores represent more suitable regions for constructing solar PV farms. The final map related to the
scores represent more suitable regions for constructing solar PV farms. The final map related to the
priority
of regions (1057 districts based on Iran’s political division) was calculated as depicted in
priority of regions (1057 districts based on Iran’s political division) was calculated as depicted in
Figure
7.
Figure 7.
(a)
(b)
(c)
(d)
Figure 5. Cont.
Figure 5. Cont.
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15 of 23
(e)
(f)
(g)
(h)
(i)
(j)
(k)
Figure 5. Evaluation criteria layers for land Suitability analysis: (a) elevation criterion; (b) slope
Figure 5. Evaluation criteria layers for land Suitability analysis: (a) elevation criterion; (b) slope
criterion; (c) solar radiation criterion; (d) average annual temperature criterion; (e) average annual
criterion; (c) solar radiation criterion; (d) average annual temperature criterion; (e) average annual
humidity criterion; (f) average annual of cloudy days criterion; (g) average annual of dusty days
humidity criterion; (f) average annual of cloudy days criterion; (g) average annual of dusty days
criterion; (h) distance from residential area criterion; (i) distance from access road criterion; (j) land
criterion; (h) distance from residential area criterion; (i) distance from access road criterion; (j) land use
use criterion; and (k) distance from power transmission lines criterion.
criterion; and (k) distance from power transmission lines criterion.
Energies 2016, 9, 643
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16 of 23
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Figure 6.
6. Classified
Classified land
land suitability
suitability map
map for
for exploiting
exploiting solar
solarPV
PVfarms.
farms.
Figure
Figure 6. Classified land suitability map for exploiting solar PV farms.
Figure 7. The priority of various districts for exploiting solar PV farms.
Figure
Figure 7.
7. The
The priority
priority of
of various
various districts
districts for
for exploiting
exploiting solar
solar PV
PV farms.
farms.
5. Results and Discussion
5. Results and Discussion
The obtained results indicated that the combination of FAHP, SAW with GIS has a high accuracy
in land
suitabilityresults
analysis
modeling
and,
in this way,
climatic
criteria
havehas
the
mostaccuracy
relative
The obtained
indicated
that the
combination
of FAHP,
SAW
with GIS
a high
importance.
in
land suitability analysis modeling and, in this way, climatic criteria have the most relative
importance.
Energies 2016, 9, 643
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5. Results and Discussion
The obtained results indicated that the combination of FAHP, SAW with GIS has a high accuracy in
land suitability analysis modeling and, in this way, climatic criteria have the most relative importance.
The results revealed that 14.7%, 17.2%, 19.2%, 11.3% and 1.8% of Iran’s area are positioned as
excellent, good, fair, low and poor classes, respectively.
About 35.8% of the areas in Iran are in the restricted area (Figure 2). Based on this, we can say
that this is due to the high slope or elevation. As mentioned earlier, the regions with slope greater
than 11% as well as above 2200 m from sea level are unsuitable for the construction of PV farms.
Hence, the regions with restricted areas that are not suitable for creating large farms may have a better
situation for implementation of small off-grid solar technology.
Table 3 shows the results for Iran’s provinces based on the percentage of area related to each
defined class. As indicated in Table 3, Golestan, Ardabil, North Khorasan, Gilan and Mazandaran
provinces have the highest area in the poor class due to the rain forest areas in the northern part of
Iran. Moreover, as the suitable provinces, Kerman, Yazd, Fars, Sisitan and Baluchestan, Southern
Khorasan and Isfahan, with half the total area of Iran, are included in the regions with the most
excellent suitability for exploiting solar energy. Area classification of the most suitable provinces based
on the percentage of area related to each class including excellent, good, fair, low and poor, as well as
unsuitable area is presented in Figure 8.
Table 3. The percentage of area related to various classes at the provincial level.
Province
Poor (%)
Low (%)
Fair (%)
Good (%)
Excellent (%)
Unsuitable (%)
Kerman
Yazd
South Khorasan
Sistan and Baluchestan
Isfahan
Fars
Qom
Chaharmahal and Bakhtiari
Markazi
Alborz
Tehran
Hormozgan
Semnan
Razavi Khorasan
Hamadan
Kohgeluyeh and BoyerAhmad
Qazvin
Bushehr
Lorestan
East Azerbaijan
West Azerbaijan
Kermanshah
Kordestan
Ilam
North Khorasan
Khuzestan
Zanjan
Mazandaran
Golestan
Gilan
Ardabil
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.34
0.00
0.04
0.32
3.45
0.00
0.02
3.01
0.17
0.00
6.11
3.13
0.00
0.00
0.36
19.54
0.72
1.82
5.45
31.24
11.73
23.52
1.77
1.11
5.45
3.76
5.15
2.98
2.64
1.19
4.57
6.81
9.41
14.10
10.11
24.90
3.84
3.48
20.41
21.74
4.15
34.47
32.03
6.93
7.96
27.59
27.01
29.52
28.14
14.15
29.83
17.13
43.53
9.83
9.00
14.51
20.87
15.97
11.37
25.60
3.90
32.35
20.12
22.64
23.48
23.15
33.08
38.08
11.63
28.52
32.98
21.19
19.83
19.45
33.96
41.44
23.45
19.17
26.75
33.50
1.52
2.80
2.02
5.12
18.96
23.37
21.95
22.86
25.29
20.94
30.18
20.66
26.87
9.81
14.53
11.53
19.62
13.79
35.95
11.79
13.54
11.72
15.96
5.57
6.68
17.27
15.23
5.13
3.52
8.67
7.03
0.07
0.02
0.00
0.30
38.48
29.99
24.25
20.79
19.70
18.59
17.53
12.93
12.19
6.40
6.03
6.03
5.66
5.11
5.08
4.79
4.45
3.35
2.40
2.17
1.48
1.44
1.39
0.76
0.75
0.69
0.11
0.00
0.00
0.00
0.00
30.96
36.52
33.84
31.71
33.87
46.12
24.05
61.31
24.02
56.51
47.39
44.83
41.13
19.66
17.05
68.29
30.07
30.03
56.30
31.86
37.24
40.39
33.98
42.70
30.02
33.65
29.39
78.81
36.11
69.11
27.52
Due to the geographical location and climatic conditions of central and southern regions of
Iran such as aridity, high quantity of sunny days and good situation for other defined criteria in
this research, Kerman, Yazd, Fars, Sisitan and Baluchestan, Southern Khorasan and Isfahan will be
Energies 2016, 9, 643
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attractive for investment in solar energy projects. Figure 9 presents a closer view of the geographical
location and classification in these provinces.
Due to obtained data in this research, we can also determine the prioritization in country, districts
or in rural area level for Iran. More detail analysis, including the 50 best districts in various provinces
are
presented in Appendix 6.
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Figure 8. Area classification of the most suitable provinces.
Figure 8. Area classification of the most suitable provinces.
Figure 8. Area classification of the most suitable provinces.
Figure 9. Closer view of the geographical location and classification in the most suitable provinces.
Figure 9. Closer view of the geographical location and classification in the most suitable provinces.
Figure 9. Closer view of the geographical location and classification in the most suitable provinces.
6. Conclusions
6. Conclusions
As a developing country and considering the growth of energy consumption, Iran needs to
As aand
developing
country
considering
the Unavoidably,
growth of energy
consumption,
needs
to
develop
increase its
energyand
generation
capacity.
it should
consider aIran
special
status
develop
and
increase
its
energy
generation
capacity.
Unavoidably,
it
should
consider
a
special
status
for planning and providing energy in its developmental plan. Extensive utilization of fossil energy
for
planning
and economic
providing sectors
energy has
in itsled
developmental
Extensive
utilization
of fossilofenergy
resources
across
to increase ofplan.
pollutants
as well
as depletion
these
resources across
economic
sectors
to of
increase
of pollutants
as well as
depletion
of these
resources.
Thus, the
inclination
for has
the led
usage
renewable
energy resources
should
be taken
into
resources.
Thus,
the
inclination
for
the
usage
of
renewable
energy
resources
should
be
taken
into
consideration due to characteristics such as renewability, availability, pollution reduction, and, more
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6. Conclusions
As a developing country and considering the growth of energy consumption, Iran needs to
develop and increase its energy generation capacity. Unavoidably, it should consider a special status
for planning and providing energy in its developmental plan. Extensive utilization of fossil energy
resources across economic sectors has led to increase of pollutants as well as depletion of these resources.
Thus, the inclination for the usage of renewable energy resources should be taken into consideration
due to characteristics such as renewability, availability, pollution reduction, and, more importantly,
sustainable development. Application of renewable energies including solar energy can assist Iran in
achieving goals including diversification of energy mix, application of local energy resources, reducing
environmental impacts, and eventually sustainable development of the energy sector.
Because of high initial capital investment to set up solar power plants, identification of the best
sites for exploitation of solar energy may be the most important step in the development of this
industry. This study provides a practical approach, considering technical, environmental, geographical,
and economic criteria, to assess and prioritize various regions of Iran for exploiting solar energy using
geographical information system (GIS) and fuzzy AHP technique.
The results indicated that 14.7% of the entire lands in Iran have a suitability level of excellent,
17.2% are in good level, 19.2% are in fair level, 11.3% are in low level, and 1.8% are in poor level
with the lowest priority regarding installation of photovoltaic. Moreover, provinces of Kerman, Yazd,
Fars, Sisitan and Baluchestan, Southern Khorasan and Isfahan have the greatest Suitability for the
exploitation of solar energy, while districts of Narmashir, Fahraj, Pariz, Anar, Bam and Chatrud in
Kerman province; Safa Shahr district in Fars; Shahraki and Naroki in Sistan; and Ashkzar district in
Yazd province are among the most highly prioritized regions for development of the technology of
solar energy (Appendix 6).
In order to relate the investigations to real life situations, we consider climatic, executive, technical,
and technological limitations in land Suitability modeling in our analyses. For example, due to entrance
of dust systems in west and south regions of Iran over the past few years, the factor of the annual
average of dusty days has been taken into consideration as one of the influential factors.
Regarding the issues of development of solar industry, considering the different limitations ahead
of the government, the results of this study can be of interest to the energy planners because of
the accuracy of investigations and the obtained results. In addition to the central government and
national organizations, the results of this research can assist the relevant authorities and planners at the
level of each province together with the investors over the energy area for planning and developing
this industry. However, government’s aids to provide promotional tools like subsidies, guaranteed
purchase contracts and providing the economic situation for private part venture will encourage
investors inside and outside the country to invest in solar energy development projects.
Author Contributions: Ehsan Noorollahi acquired data. Ehsan Noorollahi and Dawud Fadai drafted the
manuscript. Ehsan Noorollahi, Dawud Fadai, Mohsen Akbarpour Shirazi and Seyed Hassan Ghodsipour
contributed to the conception and design of the study. All authors analyzed and interpreted the data and
participated in revising and finalizing of the paper.
Conflicts of Interest: The authors declare no conflict of interest.
Energies 2016, 9, 643
20 of 24
Abbreviations
The following abbreviations are used in this manuscript:
RES
PV
CSP
GIS
TNF
MCDM
MADM
MODM
FAHP
SAW
LSI
ANN
DSS
kWh·m−2 ·year−1
kWh
GWh
MW
MWh
◦C
Renewable Energy Sources
photovoltaics
Concentrating Solar Power
Geographic information system
Triangular Fuzzy Numbers
Multi Criteria Decision Making
Multi Attribute Decision Making
Multi Objective Decision Making
Fuzzy Analytic Hierarchy Process
Simple Additive Weight
Land suitability Index
Artificial Neural Network
Decision support system
kilowatt hour per square meter per year
kilowatt hour
Gigawatt hour
megawatt
Megawatt hour
Degree Celsius
Appendix A
The Specification of the Best 50 Districts for Exploitation of Solar Energy in Iran
Due to the high volume of total obtained results in this study, and in order to prepare detailed and
operational outputs, the results of the best 50 districts of Iran, based on the possession of the highest
proportion of excellent class is presented in Table A1. For each district, the name of its related county
and province, its area percentage of each class and its total area in (km2 ) are presented.
Energies 2016, 9, 643
21 of 24
Table A1. The specification of the best 50 districts for exploitation of solar energy in Iran.
No.
District
County
Province
Unsuitable Area
Poor
Low
Fair
Good
Excellent
Total Area (km2 )
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Narmashir
Fahraj
Pariz
Anar
Safa shahr c.d
Shahraki v Naroki
Central district
Chatrud
Ashkzar Central district
Central district
Khusf
Marvast
Central district
Central district
Yazdan shahr.
Central district
Koshkuiyeh
Central district
Central district
Central district
Nir
Rayen
Shahdad
Jolgeh
Kuhbonan
Central district
Roudab
Central district
Bam pasht
Kuhpayeh
Garkan-e Jonubi
Central district
Central district
Central district
Zarqan
Bam
Fahraj
Sirjan
Anar
Khorrambid
Zehak
Bam
Kerman
Sadugh
Rafsanjan
Birjand
Khatam
Yazd
Zarand
Zarand
Abarkuh
Rafsanjan
Shahr-e-babak
Marvdasht
Eqlid
Taft
Kerman
Kerman
Isfahan
Kuhbonan
Sirjan
Bam
Meybud
Saravan
Isfahan
Mobarakeh
Isfahan
Borujen
Ravar
Shiraz
Kerman
Kerman
Kerman
Kerman
Fars
Sistan and Baluchestan
Kerman
Kerman
Yazd
Kerman
South Khorasan
Yazd
Yazd
Kerman
Kerman
Yazd
Kerman
Kerman
Fars
Fars
Yazd
Kerman
Kerman
Isfahan
Kerman
Kerman
Kerman
Yazd
Sistan v baluchestan
Isfahan
Isfahan
Isfahan
Chaharmahal v bakhtiari
Kerman
Fars
0.02
0.11
0.18
0.21
0.18
0.19
0.23
0.24
0.37
0.33
0.07
0.24
0.38
0.38
0.25
0.26
0.26
0.21
0.31
0.31
0.12
0.23
0.21
0.44
0.41
0.30
0.19
0.25
0.13
0.27
0.43
0.43
0.17
0.51
0.42
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.11
0.04
0.02
0.00
0.00
0.03
0.00
0.02
0.00
0.00
0.00
0.03
0.01
0.00
0.00
0.02
0.17
0.03
0.01
0.01
0.03
0.01
0.02
0.00
0.01
0.01
0.05
0.05
0.05
0.11
0.13
0.09
0.10
0.00
0.05
0.20
0.13
0.02
0.05
0.17
0.14
0.18
0.22
0.14
0.14
0.34
0.22
0.27
0.06
0.10
0.20
0.15
0.24
0.38
0.25
0.07
0.10
0.35
0.03
0.11
0.96
0.84
0.77
0.74
0.71
0.69
0.67
0.67
0.63
0.62
0.62
0.59
0.58
0.57
0.57
0.56
0.55
0.55
0.55
0.54
0.54
0.52
0.51
0.50
0.49
0.49
0.49
0.49
0.48
0.47
0.47
0.47
0.46
0.45
0.45
528.58
3583.39
1681.77
2088.55
1879.43
781.85
6311.70
1198.05
718.69
4418.15
15,795.74
5147.89
1628.82
4034.55
2084.14
2351.22
1425.63
13,528.07
1132.75
4269.91
2042.48
2591.99
29,373.10
1820.39
2037.42
10,997.23
1861.20
1228.16
5580.34
3039.88
199.94
1549.51
868.63
8448.77
828.37
Energies 2016, 9, 643
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Table A1. Cont.
No.
District
County
Province
Unsuitable Area
Poor
Low
Fair
Good
Excellent
Total Area (km2 )
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
Mahan
Rigan
Central district
Sedeh
Pir bakran
Central district
Central district
Nuq
Central district
Mashhad marghab
Central district
Hiduj
Gogan
Shib ab
Karbal
Kerman
Rigan
Bonab
Qaen
Falavarjan
Chadegan
Saravan
Rafsanjan
Nazarabad
Pasargad
Abadeh
Sib o Soran
Azarshahr
Zabol
Shiraz
Kerman
Kerman
East azerbaijan
South Khorasan
Isfahan
Isfahan
Sistan v baluchestan
Kerman
Alborz
Fars
Fars
Sistan V Baluchestan
East Azerbaijan
Sistan v baluchestan
Fars
0.41
0.12
0.06
0.20
0.46
0.27
0.21
0.26
0.20
0.33
0.34
0.12
0.06
0.43
0.39
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.13
0.07
0.00
0.00
0.00
0.00
0.00
0.06
0.00
0.00
0.00
0.04
0.00
0.00
0.00
0.18
0.27
0.03
0.00
0.00
0.03
0.00
0.13
0.03
0.01
0.25
0.10
0.03
0.03
0.15
0.13
0.16
0.34
0.11
0.30
0.33
0.33
0.19
0.22
0.25
0.22
0.40
0.14
0.19
0.45
0.44
0.44
0.44
0.44
0.43
0.42
0.42
0.42
0.41
0.41
0.40
0.40
0.40
0.39
1919.31
7091.51
773.73
2252.91
121.61
824.98
2560.98
2279.29
287.81
1509.22
5637.03
2301.28
268.58
4871.46
19.70
Energies 2016, 9, 643
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